Abstract

In social activities, people are interested in some statistical data, such as purchase records, monthly consumption, and health data, which are usually utilized in recommendation systems. And it is seductive for them to acquire the ranking of these data among friends or other communities. In the meantime, they want their privacy data to be confidential. Therefore, a strategy is presented to allow users to obtain the result of calculating their privacy data while preserving these data. In this method, firstly a polynomial approximation function model is set up for each user. Afterwards, “fragment” the coefficients of each model into pieces. Eventually “blend” all scraps to build the global model of all users. Users can use the global model to gain their corresponding ranking results after a special computing. Security analyses of three aspects elaborate the validity of proposed privacy method, even if some spiteful attackers try to steal private data of users, no matter who they are (users or someone outside the community). Experiments results manifest that the global model competently fits all users data and all privacy data are protected.